What is RAG (retrieval-augmented generation)?
Retrieval-augmented generation (RAG) is a way of building AI assistants that first look up the most relevant passages in your own documents and records, then answer using only those passages and cite them. The model does not need to have been trained on your data. It reads what was retrieved, at the moment of the question.
This matters because a general model knows nothing about your policies, prices or patients. Asked anyway, it guesses. With RAG the answer is grounded in your content, and when nothing relevant is found, the assistant can say so instead of inventing something.
RAG also keeps costs and exposure down. Only the passages needed for a question are sent to the model, and the documents themselves stay in storage you control.
- Grounded answers that cite the source document
- Only relevant passages sent, so costs stay controlled
- Documents stay in infrastructure you control
From scattered documents to a working assistant
A useful assistant starts with the questions people really ask and the documents that should answer them. We get retrieval right first, then add answers with sources and human review.
1. Collect the questions
We gather the questions people actually ask, from staff, inboxes and call logs.
2. Audit the content
We check which documents answer them, which are out of date and what is missing.
3. Build retrieval first
We tune how content is split and searched until the right passages come back for your questions.
4. Add answers and review
The model answers from retrieved passages with sources, and a person reviews where it matters.
5. Launch and monitor
We track failed and low-rated questions and fix the content or retrieval behind them.
Why document assistants give poor answers
When a RAG assistant gets things wrong, the cause is usually in retrieval, not the model.
Out-of-date documents
Old and new versions of a policy sit side by side, and the assistant quotes the wrong one.
The wrong passage is found
Documents are split carelessly, so the passage retrieved lacks the detail that answers the question.
No sources shown
Staff cannot tell where an answer came from, so they either trust it blindly or ignore it.
Access ignored
Everyone can retrieve everything, including records they should not see.
Answers when it should not
When nothing relevant is found, the assistant fills the gap instead of saying so.
Never measured
Nobody tracks which questions fail, so the same gaps stay open for months.
What we build
- 01
Staff knowledge assistants
Policies, procedures and manuals made searchable in plain language, with the source passage shown.
- 02
Customer-facing assistants
Answers for customers, patients or students from approved content only, with hand-off to a person.
- 03
Assistants on case records
Look up and summarise a customer's or patient's history across records, respecting who may see what.
- 04
Document ingestion pipelines
New and updated documents picked up automatically, with old versions retired.
- 05
Access-aware retrieval
Retrieval filtered by role, so each user only gets answers from content they are allowed to see.
- 06
Answer quality tracking
Unanswered and low-rated questions logged, so missing content and weak retrieval get fixed.
RAG, or something simpler?
RAG is not always the answer. We match the approach to your question and your content.
| Approach | Fits when | Watch out for |
|---|---|---|
| Rules or plain search | Questions are predictable and the answers are short and fixed | Breaks down when people phrase things differently |
| RAG | Answers live in your documents and records and change over time | Needs well-kept documents and careful retrieval design |
| Fine-tuning | You need a consistent style or format across many outputs | Does not keep facts current and costs more to update |
| Plain LLM | The task needs general knowledge, not your company's information | Will guess about anything specific to your business |
Every stage of a RAG system
| Stage | What happens | What we check |
|---|---|---|
| Ingest | Documents and records are collected from where they live | Only current, approved versions are included |
| Chunk & index | Content is split into passages and indexed for search | Passages keep enough context to stand on their own |
| Retrieve | The most relevant passages are found for each question | The right passages come back, filtered by user access |
| Answer | The model answers using only those passages and cites them | The answer matches the sources, or says nothing was found |
| Review | A person checks answers where the stakes are high | Reviewers can see sources and correct quickly |
| Monitor | Questions, answers and ratings are tracked | Failed questions lead to content or retrieval fixes |
What you can expect
Sources on every answer
Staff can open the passage an answer came from and check it in seconds.
Retrieval tested first
We measure whether the right passages are found before we judge the answers.
Says when it does not know
If nothing relevant is found, the assistant says so or hands over to a person.
Your data, your infrastructure
Documents and indexes stay in systems you control. Hosting is confirmed per project.
Where to go next
LLM Development Services
LLM development and integration for drafting, summarising, extraction and assistants, with model choice, evaluation, guardrails, cost control and private hosting.
Learn moreAI Agent Development
AI agent development for clear, checkable jobs: answering from approved information, triage, follow-ups, drafts and record look-ups, with hand-off to a named person.
Learn moreAI Agents in Healthcare
AI agents in healthcare that answer patient queries, triage calls and messages, chase follow-ups and help staff search records, with a person signing off.
Learn more
Frequently asked questions
Policies, manuals, procedures, FAQs, contracts, case notes and records from your systems. Text-based files work best. Scanned documents need text extraction first.
No. RAG looks up the relevant passages at the moment of the question and gives them to the model. Your documents are not used to train it.
We set up ingestion so new and changed documents are picked up automatically and old versions retired. The answer changes as soon as the document does.
Yes. Retrieval is filtered by role, so a user only gets answers drawn from content they are already allowed to see.
The assistant says it could not find it, and can hand the question to a named person. Those gaps are logged so you can add the missing content.
Running costs depend on usage and the model chosen. RAG helps keep them controlled, because only the few passages relevant to a question are sent to the model, and we monitor usage from day one.
Yes, with access control, audit logging and data kept in infrastructure you control. A clinician reviews anything that reaches a patient or their record.
Send us the questions your team keeps asking
We will check whether your documents can answer them, and whether RAG or something simpler is the right fit.
Or write directly to support@algonsolutions.com